Project Associate

We are looking for a passionate and talented Machine Learning Engineer with expertise in Speech Processing to join our team. This role offers the opportunity to work on cutting-edge technologies, develop state-of-the-art solutions, and make a real impact in the field of audio and speech applications.

Educational Qualifications:

• Master’s or PhD in Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related field with a specialization in Speech Processing.

Key Responsibilities:

• Design and develop advanced machine learning algorithms and pipelines for audio and speech generative models, aiming to achieve state-of-the-art (SOTA) performance.

• Work hands-on with frameworks for 

  Speaker Diarization

  , 

  Source Separation

  , 

  Noise Cancellation

  , 

  Speaker Recognition

  , and 

  Automatic Speech Recognition (ASR)

  .

• Should be able to build 

  text-to-speech (TTS)

   synthesizers along with integrated features like voice cloning Emotion Control, Accent Flexibility, Pitch variation, and Expressivity.

• Research and implement 

  speech-to-speech translation (S2ST)

   systems with a focus on improving accuracy and efficiency.

• Conduct experiments on large-scale datasets and address challenges related to domain-shifted conditions.

• Collaborate with cross-functional teams to integrate speech models into real-world applications.

• Stay up-to-date with the latest advancements in speech processing and explore ways to apply them effectively.

Requirements:

• Proven experience in implementing and optimizing SOTA frameworks for 

  Speaker Diarization

  , 

  Source Separation

  , 

  Noise Cancellation

  , 

  Speaker Recognition

  , and 

  ASR

  .
• Must have experience working with SOTA 

  TTS

   frameworks or models, along with a solid understanding of integrating advanced features such as Voice Cloning (including zero-shot, cross-lingual, and code-switched speech generation), Emotion Control, Accent Flexibility, Pitch Variation, and Expressivity.
• Practical knowledge of handling large-scale datasets and managing experiments under domain shifted conditions.
• Expertise in 

  S2ST

  systems and their 

  end-to-end

   development.
• Strong programming skills in Python and experience with frameworks such as TensorFlow or PyTorch.